Exploring the Potential of Low-Barrier AI Tools for Culturally Responsive STEM Learning: Early Māori and Pacific Learner Insights
Date
Authors
Williams, Toiroa
Nguyen, Minh
Ka'ai, Tania
Vallayil, Manju
Tukimata, Nogiata
Smith-Henderson, Tania
Supervisor
Item type
Journal Article
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Journal Title
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Volume Title
Publisher
MDPI AG
Abstract
Recent advances in large language models (LLMs) have enabled new forms of software creation through natural-language interaction. However, many AI-assisted coding tools continue to assume familiarity with development environments, programming workflows, and technical conventions, which may limit accessibility for early-stage learners and communities historically underrepresented in digital participation. This challenge is particularly relevant in Aotearoa New Zealand, where Māori and Pacific peoples remain underrepresented across STEM and technology pathways. This paper introduces TechTahi, a browser-based, syntax-free AI-assisted platform designed to support low-barrier digital creation through natural-language prompts and immediate in-browser previews. The study had two aims: to describe the design rationale and workflow of TechTahi and to explore early learner perceptions following initial use of the platform. An exploratory pilot design was employed. Five participants completed a post-use survey after hands-on interaction with TechTahi. Responses were analysed descriptively, with open-ended feedback reviewed for recurring themes. Findings suggested generally positive perceptions of accessibility and ease of use, particularly the ability to create working applications without prior coding knowledge. Participants also identified opportunities for culturally relevant features, including language support and locally meaningful design elements, alongside areas for improvement such as clearer onboarding guidance and reduced information density. These preliminary findings suggest that syntax-free, culturally responsive AI creation tools may offer promising pathways for widening participation in digital learning. Further research with larger and more diverse samples is needed to evaluate longer-term educational impact.Description
Keywords
1301 Education Systems, 1302 Curriculum and Pedagogy, 1303 Specialist Studies in Education, 3901 Curriculum and pedagogy, 3902 Education policy, sociology and philosophy, 3904 Specialist studies in education, Māori and Pacific learners, STEM education, Mātauranga Māori, culturally responsive pedagogy, Indigenous knowledge systems, generative AI in education, large language models, AI-enabled learning environments, syntax-free and no-code programming, end-user programming, community computing
Source
Education Sciences, ISSN: 2227-7102 (Print); 2227-7102 (Online), MDPI AG, 16(5), 808-823. doi: 10.3390/educsci16050808
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.